Training-Free Object-Agnostic Jam Detection in Fulfillment Centers
In fulfillment centers, diverse objects move continuously from inbound to outbound operations and can become jammed due to excessive conveyor friction, incorrect orientation, or mechanical failures. Traditional jam detection approaches rely on object detection models to identify objects, followed by tracking algorithms (such as IoU overlap and Kalman filtering) to monitor motion over time. This pipeline requires thousands of manual annotations, consuming approximately two weeks of effort, and is limited to annotated object classes. We present a training-free, object-agnostic jam detection method that eliminates the need for labeled data. Our approach uniformly samples reference points wit...
| Authors | Ruiliang Liu, Tina Dongxu Li, Joshua Migdal et al. |
|---|---|
| Primary category | cs.CV |
| Published | 29 May 2026 |
| Last updated at source | 2 June 2026 |
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